Meta-Analysis of Neoadjuvant Immunotherapy for Patients with Resectable Non-Small Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: Immunotherapy has created a paradigm shift in the treatment of metastatic non-small cell lung cancer (NSCLC), overcoming the therapeutic plateau previously achieved by systemic chemotherapy. There is growing interest in the utility of immunotherapy for patients with resectable NSCLC in the neoadjuvant setting. The present systematic review and meta-analysis aim to provide an overview of the existing evidence, with a focus on pathological and radiological response, perioperative clinical outcomes, and long-term survival. METHODS: A systematic review was conducted using electronic databases from their dates of inception to August 2021. Pooled data on pathological response, radiological response, and perioperative outcomes were meta-analyzed where possible. RESULTS: Eighteen publications from sixteen studies were identified, involving 548 enrolled patients who underwent neoadjuvant immunotherapy, of whom 507 underwent surgery. Pathologically, 52% achieved a major pathological response, 24% a complete pathological response, and 20% reported a complete pathological response of both the primary lesion as well as the sampled lymph nodes. Radiologically, 84% of patients had stable disease or partial response. Mortality within 30 days was 0.6%, and morbidities were reported according to grade and frequency. CONCLUSION: The present meta-analysis demonstrated that neoadjuvant immunotherapy was feasible and safe based on perioperative clinical data and completion rates of surgery within their intended timeframe. The pathological response after neoadjuvant immunotherapy was superior to historical data for patients who were treated with neoadjuvant chemotherapy alone, whilst surgical and treatment-related adverse events were comparable. The limitations of the study included the heterogenous treatment regimens, lack of long-term follow-up, variations in the reporting of potential prognostic factors, and potential publication bias.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.049 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".